CN113327241A - Visual detection method and system for surface defects of bearing end face - Google Patents
Visual detection method and system for surface defects of bearing end face Download PDFInfo
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- G06T7/0004—Industrial image inspection
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10004—Still image; Photographic image
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- G06T2207/20—Special algorithmic details
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- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20092—Interactive image processing based on input by user
- G06T2207/20104—Interactive definition of region of interest [ROI]
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20112—Image segmentation details
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30108—Industrial image inspection
Abstract
The invention discloses a visual detection method for surface defects of a bearing end face, which comprises the following steps: acquiring a bearing end face picture; preprocessing the bearing end face picture to obtain a preprocessed bearing end face picture; acquiring an interested area of the preprocessed bearing end face picture; obtaining a plurality of same local sub-images of the region of interest by equally dividing the region of interest; respectively inputting a plurality of local sub-images of the region of interest into a bearing end surface defect visual inspection depth learning model to obtain a plurality of local sub-image detection results; the bearing end surface defect visual detection depth learning model is constructed by taking historical local subimages of an interested region at different angles as input and taking a historical local subimage detection result as output; and splicing the detection results of the plurality of local sub-images to obtain a surface defect picture of the end face of the bearing. The invention greatly improves the detection precision of the surface defects of the bearing end face.
Description
Technical Field
The invention relates to the field of bearing defect detection, in particular to a visual detection method and system for surface defects of a bearing end face.
Background
The bearing is mainly used for bearing the weight of mechanical equipment and providing accurate guide for the rotation of the automobile hub, bears axial load and radial load, and is a very important component in the mechanical equipment. Because bearings play an important role in the use of mechanical equipment, bearing purchasers have stringent requirements for the quality of the bearings. In order to meet the requirements of bearing purchasers, bearing manufacturers can strictly check the quality of produced bearings. However, the existing bearing quality detection method still largely adopts an artificial visual observation method to detect the surface defects of the bearing end face, and the detection method has low detection efficiency and high omission and false detection rates.
The machine vision defect detection method is used as an automatic detection technology and applied to bearing defect detection, not only has high detection efficiency, but also can greatly reduce the labor cost, but the existing machine vision defect detection method usually adopts the traditional machine vision technology to detect the surface defects of the bearing end face, however, when the shooting angles of cameras are different, the forms detected by the same defect are different, so that the detection result has errors, and the detection precision of the surface defects of the bearing end face is low at present.
Disclosure of Invention
The invention aims to provide a visual detection method and a visual detection system for surface defects of a bearing end face, which aim to solve the problem of low detection precision of the surface defects of the bearing end face in the prior art.
In order to achieve the purpose, the invention provides the following scheme:
a visual inspection method for surface defects of a bearing end face comprises the following steps:
acquiring a bearing end face picture;
preprocessing the bearing end face picture to obtain a preprocessed bearing end face picture;
obtaining an interested area of the preprocessed bearing end face picture;
obtaining a plurality of same local sub-images of the region of interest by equally dividing the length and the width of the region of interest;
respectively inputting the local sub-images of the region of interest into a bearing end surface defect visual inspection deep learning model to obtain a plurality of local sub-image detection results; the local sub-image detection result comprises a defect form and a defect position; the bearing end surface defect visual detection deep learning model is constructed by taking historical local sub-images of the region of interest at different angles as input and taking the detection result of the historical local sub-images as output;
and splicing the detection results of the local sub-images to obtain a surface defect picture of the end face of the bearing.
Optionally, the bearing end surface defect visual inspection deep learning model specifically includes:
the system comprises an input layer, a backbone network, a neck network, a self-attention network and an output layer which are connected in sequence; the main network is used for extracting the characteristics of the local sub-images of the region of interest to obtain the extracted image characteristics; the neck network is used for further extracting the characteristics of the extracted image characteristics to obtain secondarily extracted image characteristics; the self-attention network employs a self-attention mechanism for modifying pixels of the twice extracted image feature map.
Optionally, the obtaining of the region of interest of the preprocessed bearing end face picture specifically includes:
carrying out binarization processing on the preprocessed bearing end face picture by utilizing an Otsu method and eliminating a hole area to obtain a bearing end face picture eliminating the hole area;
and positioning the bearing end face picture of the hole elimination region in a communication region detection and area screening mode, and cutting out the region of interest.
Optionally, the number of local sub-images of the region of interest is an integer multiple of 2.
Optionally, the size of the local sub-image of the region of interest is 320 × 320 dpi; the local sub-image of the region of interest is a 3-channel image.
A visual inspection system for surface defects of a bearing end face, comprising:
the acquisition module is used for acquiring a bearing end face picture;
the preprocessing module is used for preprocessing the bearing end face picture to obtain a preprocessed bearing end face picture;
the interested region acquisition module is used for acquiring the interested region of the preprocessed bearing end face picture;
the local sub-image acquisition module is used for performing equal segmentation on the length and the width of the region of interest to obtain a plurality of same local sub-images of the region of interest;
the detection module is used for respectively inputting the local sub-images of the region of interest into the bearing end surface defect visual detection deep learning model to obtain a plurality of local sub-image detection results; the local sub-image detection result comprises a defect form and a defect position; the bearing end surface defect visual detection deep learning model is constructed by taking historical local sub-images of the region of interest at different angles as input and taking the detection result of the historical local sub-images as output;
and the splicing module is used for splicing the detection results of the local subimages to obtain a surface defect picture of the end face of the bearing.
Optionally, the bearing end surface defect visual inspection deep learning model specifically includes:
the system comprises an input layer, a backbone network, a neck network, a self-attention network and an output layer which are connected in sequence; the main network is used for extracting the characteristics of the local sub-images of the region of interest to obtain the extracted image characteristics; the neck network is used for further extracting the characteristics of the extracted image characteristics to obtain secondarily extracted image characteristics; the self-attention network employs a self-attention mechanism for modifying pixels of the twice extracted image feature map.
Optionally, the region of interest obtaining module specifically includes:
the hole eliminating unit is used for carrying out binarization processing on the preprocessed bearing end face picture by utilizing the Otsu method and eliminating a hole area to obtain the bearing end face picture with the hole area eliminated;
and the cutting unit is used for positioning the bearing end face picture of the hole elimination region in a communication region detection and area screening mode and cutting out the region of interest.
Optionally, the number of local sub-images of the region of interest is an integer multiple of 2.
Optionally, the size of the local sub-image of the region of interest is 320 × 320 dpi; the local sub-image of the region of interest is a 3-channel image.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention provides a visual detection method and a system for surface defects of a bearing end face, which are characterized in that a plurality of local subimages of an interested area are respectively input into a visual detection deep learning model for the surface defects of the bearing end face to obtain a plurality of local subimages detection results, and then the local subimages detection results are spliced to obtain a surface defect picture of the bearing end face.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a flow chart of a visual inspection method for surface defects of a bearing end face according to the present invention;
FIG. 2 is a schematic view of a visual inspection system for surface defects on the end face of a bearing according to the present invention;
FIG. 3 is a flow chart of a visual inspection method for surface defects of a bearing end face picture collected by a CCD camera according to the present invention;
FIG. 4 is a top view of a bearing end face photographed by a CCD camera provided in the present invention, FIG. 4(a) is a top view of a non-defective end face, and FIG. 4(b) is a top view of an end face including a defect;
FIG. 5 is a view of the bearing region of interest corresponding to FIG. 4(b) provided by the present invention;
FIG. 6 is a graph of the equivalent segmentation result of the bearing region of interest provided by the present invention;
FIG. 7 is a diagram of a deep learning model architecture for visual inspection of surface defects on bearing end faces according to the present invention;
FIG. 8 is a diagram of a self-attention network architecture provided by the present invention;
FIG. 9 is a diagram showing the results of surface defect inspection of the end faces of bearings obtained by the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
As shown in fig. 1, the present invention provides a visual inspection method for surface defects of a bearing end face, comprising:
step 101: and acquiring a bearing end face picture. The bearing end face picture can be collected through a CCD industrial camera.
Step 102: and preprocessing the bearing end face picture to obtain a preprocessed bearing end face picture. The preprocessing can adopt image preprocessing methods such as gray level transformation, Gaussian filtering and the like.
Step 103: and acquiring the region of interest of the preprocessed bearing end face picture.
Step 104: and equally dividing the length and the width of the region of interest to obtain a plurality of same local sub-images of the region of interest. When the amount is divided into equal amounts, the division is generally 4.
Step 105: respectively inputting the local sub-images of the region of interest into a bearing end surface defect visual inspection deep learning model to obtain a plurality of local sub-image detection results; the local sub-image detection result comprises a defect form and a defect position; the bearing end surface defect visual detection deep learning model is constructed by taking historical local sub-images of the region of interest at different angles as input and taking the detection result of the historical local sub-images as output.
Step 106: and splicing the detection results of the local sub-images to obtain a surface defect picture of the end face of the bearing.
In practical application, the bearing end surface defect visual inspection deep learning model specifically comprises:
the system comprises an input layer, a backbone network, a neck network, a self-attention network and an output layer which are connected in sequence; the main network is used for extracting the characteristics of the local sub-images of the region of interest to obtain the extracted image characteristics; the neck network is used for further extracting the characteristics of the extracted image characteristics to obtain secondarily extracted image characteristics; the self-attention network employs a self-attention mechanism for modifying pixels of the twice extracted image feature map.
In practical application, the obtaining of the region of interest of the preprocessed bearing end face picture specifically includes:
carrying out binarization processing on the preprocessed bearing end face picture by utilizing an Otsu method and eliminating a hole area to obtain a bearing end face picture eliminating the hole area;
and positioning the bearing end face picture of the hole elimination region in a communication region detection and area screening mode, and cutting out the region of interest.
In practical application, the number of the local sub-images of the region of interest is an integer multiple of 2.
In practical application, the size of the local sub-image of the region of interest is 320 × 320 dpi; the local sub-image of the region of interest is a 3-channel image.
As shown in fig. 2, the present invention also provides a visual inspection system for surface defects of a bearing end face, comprising:
and the acquisition module 201 is used for acquiring a bearing end face picture.
The preprocessing module 202 is configured to preprocess the bearing end face picture to obtain a preprocessed bearing end face picture.
And the region-of-interest obtaining module 203 is configured to obtain a region of interest of the preprocessed bearing end face picture.
And the local sub-image obtaining module 204 is configured to obtain a plurality of same local sub-images of the region of interest by equally dividing the length and the width of the region of interest.
The detection module 205 is configured to input the plurality of local sub-images of the region of interest into the bearing end surface defect visual inspection deep learning model respectively to obtain a plurality of local sub-image detection results; the local sub-image detection result comprises a defect form and a defect position; the bearing end surface defect visual detection deep learning model is constructed by taking historical local sub-images of the region of interest at different angles as input and taking the detection result of the historical local sub-images as output.
And the splicing module 206 is configured to splice the plurality of local sub-image detection results to obtain a surface defect picture of the bearing end face.
In practical application, the bearing end surface defect visual inspection deep learning model specifically comprises:
the system comprises an input layer, a backbone network, a neck network, a self-attention network and an output layer which are connected in sequence; the main network is used for extracting the characteristics of the local sub-images of the region of interest to obtain the extracted image characteristics; the neck network is used for further extracting the characteristics of the extracted image characteristics to obtain secondarily extracted image characteristics; the self-attention network employs a self-attention mechanism for modifying pixels of the twice extracted image feature map.
In practical application, the region of interest obtaining module specifically includes:
and the hole eliminating unit is used for carrying out binarization processing on the preprocessed bearing end face picture by utilizing the Otsu method and eliminating the hole area to obtain the bearing end face picture without the hole area.
And the cutting unit is used for positioning the bearing end face picture of the hole elimination region in a communication region detection and area screening mode and cutting out the region of interest.
In practical application, the number of the local sub-images of the region of interest is an integer multiple of 2.
In practical application, the size of the local sub-image of the region of interest is 320 × 320 dpi; the local sub-image of the region of interest is a 3-channel image.
With reference to the foregoing technical solution, the present invention provides a specific embodiment of a visual inspection method for surface defects of a bearing end face, which can be applied to the foregoing technical solution, and is a flowchart of a visual inspection method for surface defects of a bearing end face picture acquired by a CCD camera, as shown in fig. 1, where the present invention uses the upper surface defect inspection of a bearing end face for experimental explanation:
1) pictures of each end face of the bearing are collected by a CCD camera, fig. 4 is a top surface view of the end face of the bearing photographed by the CCD camera, fig. 4(a) is a top surface view of the end face without defects, and fig. 4(b) is a top surface view of the end face including defects.
2) The collected bearing end face image is firstly subjected to image preprocessing methods such as gray level transformation and Gaussian filtering to remove noise influence, then the preprocessed image is subjected to binarization and hole area elimination by using the Otsu method, finally the area where the bearing end face is located and the bearing end face area (interested area) is cut by using means such as connected domain detection and area screening, and the result is shown in fig. 5.
3) The cut out length and width of the region of interest are equally divided to obtain local sub-images of the same size, and fig. 6 shows the 4 equally divided local sub-images.
4) And respectively inputting the acquired local sub-images of the region of interest to a bearing end surface defect visual inspection deep learning model for defect detection. The bearing end surface defect visual inspection deep learning model is constructed by introducing a self-attention network at the rear end of the bearing end surface defect visual inspection deep learning model on the basis of a structure based on a YoLov5 backbone network and a neck network. The structure of the bearing end surface defect visual inspection deep learning model is shown in fig. 7, wherein Backbnoes in fig. 7 represents a main network for extracting image features, and cock represents a Neck network for better utilizing the features extracted by the main network, the two parts form the main architecture of the YoLov5 model, and the YoLov5 main architecture is adopted here. Since the convolution operation can only calculate the target pixel in the image by using local information, and the operation loses global information and brings a certain information deviation, a self-attention mechanism is introduced and is arranged at the tail of a YoLov5 network, the self-attention mechanism structure diagram is shown in FIG. 8, wherein X represents an input feature mapping diagram, T represents batch, namely the number of feature mapping diagrams input each time, H represents the height of the feature mapping diagram, W represents the width of the feature mapping diagram, 512 and 1024 represent the number of output feature mapping diagram channels, Z represents an output result, theta, phi and g represent different convolutions, and softmax represents an activation function.
5) The output result of the bearing end surface defect visual inspection deep learning model is spliced according to the position of the local subimage detection result to be output by using a direct splicing method, so as to form a final detection result picture, as shown in fig. 9, wherein the rectangular frame marking area is the area where the defect is located.
As can be seen from the defect detection result image in FIG. 9, the present invention can realize the surface defect detection of the bearing end face, can accurately position the defect position, and has a wide application prospect in the bearing defect detection and related fields.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (10)
1. A visual inspection method for surface defects of a bearing end face is characterized by comprising the following steps:
acquiring a bearing end face picture;
preprocessing the bearing end face picture to obtain a preprocessed bearing end face picture;
obtaining an interested area of the preprocessed bearing end face picture;
obtaining a plurality of same local sub-images of the region of interest by equally dividing the length and the width of the region of interest;
respectively inputting the local sub-images of the region of interest into a bearing end surface defect visual inspection deep learning model to obtain a plurality of local sub-image detection results; the local sub-image detection result comprises a defect form and a defect position; the bearing end surface defect visual detection deep learning model is constructed by taking historical local sub-images of the region of interest at different angles as input and taking the detection result of the historical local sub-images as output;
and splicing the detection results of the local sub-images to obtain a surface defect picture of the end face of the bearing.
2. The visual inspection method for surface defects of a bearing end face according to claim 1, wherein the visual inspection deep learning model for surface defects of a bearing end face specifically comprises:
the system comprises an input layer, a backbone network, a neck network, a self-attention network and an output layer which are connected in sequence; the main network is used for extracting the characteristics of the local sub-images of the region of interest to obtain the extracted image characteristics; the neck network is used for further extracting the characteristics of the extracted image characteristics to obtain secondarily extracted image characteristics; the self-attention network employs a self-attention mechanism for modifying pixels of the twice extracted image feature map.
3. The visual inspection method for surface defects of a bearing end face according to claim 1, wherein the acquiring of the region of interest of the preprocessed bearing end face picture specifically comprises:
carrying out binarization processing on the preprocessed bearing end face picture by utilizing an Otsu method and eliminating a hole area to obtain a bearing end face picture eliminating the hole area;
and positioning the bearing end face picture of the hole elimination region in a communication region detection and area screening mode, and cutting out the region of interest.
4. The method of claim 1, wherein the number of the local sub-images of the region of interest is an integer multiple of 2.
5. The visual inspection method of surface defects of a bearing end face according to claim 1, wherein the size of the local sub-image of the region of interest is 320 x 320 dpi; the local sub-image of the region of interest is a 3-channel image.
6. A visual inspection system for surface defects on a bearing end face, comprising:
the acquisition module is used for acquiring a bearing end face picture;
the preprocessing module is used for preprocessing the bearing end face picture to obtain a preprocessed bearing end face picture;
the interested region acquisition module is used for acquiring the interested region of the preprocessed bearing end face picture;
the local sub-image acquisition module is used for performing equal segmentation on the length and the width of the region of interest to obtain a plurality of same local sub-images of the region of interest;
the detection module is used for respectively inputting the local sub-images of the region of interest into the bearing end surface defect visual detection deep learning model to obtain a plurality of local sub-image detection results; the local sub-image detection result comprises a defect form and a defect position; the bearing end surface defect visual detection deep learning model is constructed by taking historical local sub-images of the region of interest at different angles as input and taking the detection result of the historical local sub-images as output;
and the splicing module is used for splicing the detection results of the local subimages to obtain a surface defect picture of the end face of the bearing.
7. The visual inspection system for surface defects of a bearing end face according to claim 6, wherein the visual inspection deep learning model for surface defects of a bearing end face specifically comprises:
the system comprises an input layer, a backbone network, a neck network, a self-attention network and an output layer which are connected in sequence; the main network is used for extracting the characteristics of the local sub-images of the region of interest to obtain the extracted image characteristics; the neck network is used for further extracting the characteristics of the extracted image characteristics to obtain secondarily extracted image characteristics; the self-attention network employs a self-attention mechanism for modifying pixels of the twice extracted image feature map.
8. The visual inspection system for surface defects of a bearing end face as set forth in claim 6, wherein the region of interest acquisition module specifically comprises:
the hole eliminating unit is used for carrying out binarization processing on the preprocessed bearing end face picture by utilizing the Otsu method and eliminating a hole area to obtain the bearing end face picture with the hole area eliminated;
and the cutting unit is used for positioning the bearing end face picture of the hole elimination region in a communication region detection and area screening mode and cutting out the region of interest.
9. The visual inspection system for surface defects on a bearing end face as set forth in claim 1 wherein the number of local sub-images of the region of interest is an integer multiple of 2.
10. The visual inspection system for surface defects of a bearing end face according to claim 1, wherein the size of the local subimage of the region of interest is 320 x 320 dpi; the local sub-image of the region of interest is a 3-channel image.
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| CN114708484A (en) * | 2022-03-14 | 2022-07-05 | 中铁电气化局集团有限公司 | Pattern analysis method suitable for identifying defects |
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